Image Annotation

Create dataset

Search "cmd" from windows, run as administrator
.
Navigate to your Documents folder

cd "%USERPROFILE%\Documents" && cd yolo_projects

create a folder, "dataset"

mkdir dataset

Open this website, https://universe.roboflow.com/college-ojdd3/garbagedetectionapi/dataset/1
Click "Coco Jason" for downloading
unzip the folder
Copy a folder called "train", \GarbageDetectionAPI.v1i.coco\train to folder, "dataset"

like this:
Create a python file

cd dataset/train

notepad covert-jason.py

import json
import os

# Load your Roboflow COCO JSON file
with open("_annotations.coco.json") as f:
    coco = json.load(f)

# Build category lookup
categories = {cat["id"]: cat["name"] for cat in coco["categories"]}

# Build image lookup
images = {img["id"]: img for img in coco["images"]}

# Optional: define your image path prefix for Label Studio to locate the images
IMAGE_PATH_PREFIX = "/data/local-files/?d=train/"

# Collect annotations per image
annotations_by_image = {}
for ann in coco.get("annotations", []):
    img_id = ann["image_id"]
    bbox = ann["bbox"]  # [x, y, width, height]
    category_id = ann["category_id"]

    image = images[img_id]
    width = image["width"]
    height = image["height"]

    # Normalize coordinates for Label Studio
    x = bbox[0] / width * 100
    y = bbox[1] / height * 100
    w = bbox[2] / width * 100
    h = bbox[3] / height * 100

    result = {
        "from_name": "label",
        "to_name": "image",
        "type": "rectanglelabels",
        "value": {
            "x": x,
            "y": y,
            "width": w,
            "height": h,
            "rectanglelabels": [categories[category_id]],
        },
    }

    if img_id not in annotations_by_image:
        annotations_by_image[img_id] = []

    annotations_by_image[img_id].append(result)

# Convert to Label Studio format
label_studio_data = []
for img_id, image in images.items():
    entry = {
        "data": {"image": IMAGE_PATH_PREFIX + image["file_name"]},
        "annotations": [{"result": annotations_by_image.get(img_id, [])}],
    }
    label_studio_data.append(entry)

# Save to file
with open("label_studio_format.json", "w") as f:
    json.dump(label_studio_data, f, indent=2)

print("Conversion complete. Output saved to label_studio_format.json.")
print("Categories:")
for cat_id, cat_name in categories.items():
    print(f"{cat_id}: {cat_name}")

run the python file

python covert-jason.py

copy the categories from the terminal:
Going back the main folder:

cd "%USERPROFILE%\Documents" && cd yolo_projects

Activate the environment

cd venv39 && Scripts\activate

start the label-studio, (*Need to change to your folder path)

SET DATA_UPLOAD_MAX_NUMBER_FILES=10000

set LOCAL_FILES_SERVING_ENABLED=true

set LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT=%USERPROFILE%\\Documents\\yolo_projects\\datasets

set LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT=C:\\Users\\111872\\Documents\\yolo_projects\\datasets

Label-studio start

sign-up a account

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Click "Create Project"

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click the save button.
click settings
Head to the project settings and select Cloud Storage.
Click Add Source Storage and choose Local files from the Storage Type options.
Set the Absolute local path C:\Users\%USERPROFILE%\Documents\yolo_projects\datasets\train.
Select Add storage to confirm the setup.
Check Image Access: http://localhost:8080/data/local-files/?d=/train/batch_01_frame_5_jpg.rf.4b2f849f8838161c9b480a6979099fad.jpg
Go to import
find "label_studio_format.json" from "C:\Users%USERPROFILE%\Documents\yolo_projects\datasets\train"
Click "import" button

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